In the retail industry, it has long been known that product placement can greatly impact sales. For instance, in a grocery store, a product (e.g., a box of cereal) placed on a shelf at approximately eye level will tend to outsell a similar product placed on the bottom shelf. This general principle holds true in the context of ecommerce as well. When presenting item listings in a search results page, the position of an item listing within the page—particularly, the position relative to other item listings—can seriously impact the transactions (e.g., sales) resulting from the presentation of item listings that satisfy a search query. Consequently, presenting the item listings that are most likely to result in the conclusion of a transaction in the most prominent positions on the search results page can increase the number of transactions. Unfortunately, it is difficult to identify the item listings that are most likely to result in sales.
One way to assess the likelihood that an item listing will, if presented in a search results page, result in the conclusion of a transaction is to monitor certain user-initiated activities or events associated with the item listing, or, with item listings determined to be similar. For instance, if a particular item listing is presented in a list of item listings that satisfy a user's search query, and a user views the item listing, (e.g., by clicking on the item listing with a cursor control device, or otherwise selecting it), this event (referred to simply as a “view”) may be used as a measure for demand for the item offered via the item listing. Accordingly, the total number of views an item listing receives can be used as a demand metric, which in turn, can be used to predict the likelihood that an item listing will result in a transaction, if presented in the search results page. Similarly, the number of search impressions, bids (for auction item listings), watch lists, actual sales, and other events can be used as demand metrics as well. Using this general approach, with all else equal, given two item listings where the first item listing has been viewed ten times, and the other item listing viewed only once, the item listing viewed ten times would have a higher demand metric, and thus would be positioned first (e.g., at the top) of a search results page.
One problem with this approach is that the timing of the events used to derive the demand metric for the item listings is not taken into consideration. For example, referring to FIG. 1, three event timelines are shown. The event timeline with reference number 2-A shows the timing of the events 8-A (represented as vertical lines) used in deriving the demand metric for Item Listing A. Similarly, the event timelines with reference numbers 2-B and 2-C show the timing of events used in deriving the demand metrics for Item Listings B and C, respectively. For this example, the events could represent any combination of search impressions, views, bids, sales, watch lists, or other similar user-initiated actions. The graph 4 shows the value of the demand metrics for the three item listings over a period of time (e.g., 50 days). For purposes of this example, if we assume that time is measured in days, the line 6-A in the graph 4 representing the demand metric for item listing A rises relatively quickly from zero to ten with a steep slope over the first (approximately) ten days. Because the events 8-B for item listing B occurred more evenly spaced throughout days zero to fifty, the line representing the demand metric for item listing B rises from zero to ten with a more gradual slope over fifty days. Finally, for item listing C, because all ten events 8-C occur within the last (approximately) ten days, the line 6-C representing the demand metric for item listing C rises from zero to ten over the course of the final ten days.
The scenarios for which the example may be applicable are endless. However, in one scenario, Item Listing A may be for a first version of a product, whereas Item Listing C is a newly released, improved version of the same product. In such a scenario, the new and improved product associated with Item Listing C would naturally be expected to outsell the product it is replacing, associated with Item Listing A. As shown in the graph, at TIME=48 (representing day forty-eight), the demand metrics for Item Listings A, B and C are (approximately) ten, nine and seven, respectively. Despite the concentrated number of events 8-C associated with item listing C that occurred in the several days leading up to day forty-eight, and the fact that no event has occurred in the previous (approximately) thirty-eight days for Item Listing A, the demand metric for Item Listing A is greater than that of Item Listings B and C. Consequently, a better method and system for assessing demand metrics used in determining the likelihood that an item listing will result in a sale is desired.